Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations418
Missing cells414
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.3 KiB
Average record size in memory96.3 B

Variable types

Numeric6
Categorical3
Text3

Alerts

PassengerId is highly overall correlated with Unnamed: 0High correlation
Unnamed: 0 is highly overall correlated with PassengerIdHigh correlation
Age has 86 (20.6%) missing values Missing
Cabin has 327 (78.2%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
PassengerId is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
PassengerId has unique values Unique
Name has unique values Unique
SibSp has 283 (67.7%) zeros Zeros
Parch has 324 (77.5%) zeros Zeros

Reproduction

Analysis started2025-06-19 22:42:04.850533
Analysis finished2025-06-19 22:42:08.577723
Duration3.73 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.5
Minimum0
Maximum417
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-06-19T19:42:08.645723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.85
Q1104.25
median208.5
Q3312.75
95-th percentile396.15
Maximum417
Range417
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation120.81046
Coefficient of variation (CV)0.57942666
Kurtosis-1.2
Mean208.5
Median Absolute Deviation (MAD)104.5
Skewness0
Sum87153
Variance14595.167
MonotonicityStrictly increasing
2025-06-19T19:42:08.764733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
417 1
 
0.2%
0 1
 
0.2%
401 1
 
0.2%
400 1
 
0.2%
399 1
 
0.2%
398 1
 
0.2%
397 1
 
0.2%
396 1
 
0.2%
395 1
 
0.2%
394 1
 
0.2%
Other values (408) 408
97.6%
ValueCountFrequency (%)
0 1
0.2%
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
ValueCountFrequency (%)
417 1
0.2%
416 1
0.2%
415 1
0.2%
414 1
0.2%
413 1
0.2%
412 1
0.2%
411 1
0.2%
410 1
0.2%
409 1
0.2%
408 1
0.2%

PassengerId
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100.5
Minimum892
Maximum1309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-06-19T19:42:08.882216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum892
5-th percentile912.85
Q1996.25
median1100.5
Q31204.75
95-th percentile1288.15
Maximum1309
Range417
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation120.81046
Coefficient of variation (CV)0.10977779
Kurtosis-1.2
Mean1100.5
Median Absolute Deviation (MAD)104.5
Skewness0
Sum460009
Variance14595.167
MonotonicityStrictly increasing
2025-06-19T19:42:08.998845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1309 1
 
0.2%
892 1
 
0.2%
1293 1
 
0.2%
1292 1
 
0.2%
1291 1
 
0.2%
1290 1
 
0.2%
1289 1
 
0.2%
1288 1
 
0.2%
1287 1
 
0.2%
1286 1
 
0.2%
Other values (408) 408
97.6%
ValueCountFrequency (%)
892 1
0.2%
893 1
0.2%
894 1
0.2%
895 1
0.2%
896 1
0.2%
897 1
0.2%
898 1
0.2%
899 1
0.2%
900 1
0.2%
901 1
0.2%
ValueCountFrequency (%)
1309 1
0.2%
1308 1
0.2%
1307 1
0.2%
1306 1
0.2%
1305 1
0.2%
1304 1
0.2%
1303 1
0.2%
1302 1
0.2%
1301 1
0.2%
1300 1
0.2%

Pclass
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
3
218 
1
107 
2
93 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Length

2025-06-19T19:42:09.100303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:42:09.158027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring characters

ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Name
Text

Unique 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2025-06-19T19:42:09.420951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length63
Median length51
Mean length27.483254
Min length13

Characters and Unicode

Total characters11488
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique418 ?
Unique (%)100.0%

Sample

1st rowKelly, Mr. James
2nd rowWilkes, Mrs. James (Ellen Needs)
3rd rowMyles, Mr. Thomas Francis
4th rowWirz, Mr. Albert
5th rowHirvonen, Mrs. Alexander (Helga E Lindqvist)
ValueCountFrequency (%)
mr 242
 
14.0%
miss 78
 
4.5%
mrs 72
 
4.2%
john 28
 
1.6%
william 23
 
1.3%
master 21
 
1.2%
charles 16
 
0.9%
joseph 15
 
0.9%
thomas 14
 
0.8%
james 14
 
0.8%
Other values (825) 1202
69.7%
2025-06-19T19:42:09.780241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1309
 
11.4%
r 971
 
8.5%
e 822
 
7.2%
a 786
 
6.8%
s 628
 
5.5%
i 621
 
5.4%
n 596
 
5.2%
l 526
 
4.6%
M 515
 
4.5%
o 467
 
4.1%
Other values (48) 4247
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1309
 
11.4%
r 971
 
8.5%
e 822
 
7.2%
a 786
 
6.8%
s 628
 
5.5%
i 621
 
5.4%
n 596
 
5.2%
l 526
 
4.6%
M 515
 
4.5%
o 467
 
4.1%
Other values (48) 4247
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1309
 
11.4%
r 971
 
8.5%
e 822
 
7.2%
a 786
 
6.8%
s 628
 
5.5%
i 621
 
5.4%
n 596
 
5.2%
l 526
 
4.6%
M 515
 
4.5%
o 467
 
4.1%
Other values (48) 4247
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1309
 
11.4%
r 971
 
8.5%
e 822
 
7.2%
a 786
 
6.8%
s 628
 
5.5%
i 621
 
5.4%
n 596
 
5.2%
l 526
 
4.6%
M 515
 
4.5%
o 467
 
4.1%
Other values (48) 4247
37.0%

Sex
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
male
266 
female
152 

Length

Max length6
Median length4
Mean length4.7272727
Min length4

Characters and Unicode

Total characters1976
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowfemale

Common Values

ValueCountFrequency (%)
male 266
63.6%
female 152
36.4%

Length

2025-06-19T19:42:09.875767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:42:09.939640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 266
63.6%
female 152
36.4%

Most occurring characters

ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Age
Real number (ℝ)

Missing 

Distinct79
Distinct (%)23.8%
Missing86
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean30.27259
Minimum0.17
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-06-19T19:42:10.020798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile8
Q121
median27
Q339
95-th percentile57
Maximum76
Range75.83
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.181209
Coefficient of variation (CV)0.46845047
Kurtosis0.083783352
Mean30.27259
Median Absolute Deviation (MAD)8
Skewness0.45736129
Sum10050.5
Variance201.1067
MonotonicityNot monotonic
2025-06-19T19:42:10.141426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 17
 
4.1%
24 17
 
4.1%
22 16
 
3.8%
30 15
 
3.6%
18 13
 
3.1%
27 12
 
2.9%
26 12
 
2.9%
23 11
 
2.6%
25 11
 
2.6%
29 10
 
2.4%
Other values (69) 198
47.4%
(Missing) 86
20.6%
ValueCountFrequency (%)
0.17 1
 
0.2%
0.33 1
 
0.2%
0.75 1
 
0.2%
0.83 1
 
0.2%
0.92 1
 
0.2%
1 3
0.7%
2 2
0.5%
3 1
 
0.2%
5 1
 
0.2%
6 3
0.7%
ValueCountFrequency (%)
76 1
 
0.2%
67 1
 
0.2%
64 3
0.7%
63 2
0.5%
62 1
 
0.2%
61 2
0.5%
60.5 1
 
0.2%
60 3
0.7%
59 1
 
0.2%
58 1
 
0.2%

SibSp
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44736842
Minimum0
Maximum8
Zeros283
Zeros (%)67.7%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-06-19T19:42:10.229648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89675956
Coefficient of variation (CV)2.0045214
Kurtosis26.498712
Mean0.44736842
Median Absolute Deviation (MAD)0
Skewness4.1683366
Sum187
Variance0.80417771
MonotonicityNot monotonic
2025-06-19T19:42:10.304614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 283
67.7%
1 110
 
26.3%
2 14
 
3.3%
3 4
 
1.0%
4 4
 
1.0%
8 2
 
0.5%
5 1
 
0.2%
ValueCountFrequency (%)
0 283
67.7%
1 110
 
26.3%
2 14
 
3.3%
3 4
 
1.0%
4 4
 
1.0%
5 1
 
0.2%
8 2
 
0.5%
ValueCountFrequency (%)
8 2
 
0.5%
5 1
 
0.2%
4 4
 
1.0%
3 4
 
1.0%
2 14
 
3.3%
1 110
 
26.3%
0 283
67.7%

Parch
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3923445
Minimum0
Maximum9
Zeros324
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-06-19T19:42:10.375574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98142888
Coefficient of variation (CV)2.5014468
Kurtosis31.412513
Mean0.3923445
Median Absolute Deviation (MAD)0
Skewness4.6544617
Sum164
Variance0.96320264
MonotonicityNot monotonic
2025-06-19T19:42:10.449919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 324
77.5%
1 52
 
12.4%
2 33
 
7.9%
3 3
 
0.7%
4 2
 
0.5%
9 2
 
0.5%
6 1
 
0.2%
5 1
 
0.2%
ValueCountFrequency (%)
0 324
77.5%
1 52
 
12.4%
2 33
 
7.9%
3 3
 
0.7%
4 2
 
0.5%
5 1
 
0.2%
6 1
 
0.2%
9 2
 
0.5%
ValueCountFrequency (%)
9 2
 
0.5%
6 1
 
0.2%
5 1
 
0.2%
4 2
 
0.5%
3 3
 
0.7%
2 33
 
7.9%
1 52
 
12.4%
0 324
77.5%

Ticket
Text

Distinct363
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2025-06-19T19:42:10.697774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.8755981
Min length3

Characters and Unicode

Total characters2874
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique321 ?
Unique (%)76.8%

Sample

1st row330911
2nd row363272
3rd row240276
4th row315154
5th row3101298
ValueCountFrequency (%)
pc 32
 
5.9%
c.a 19
 
3.5%
soton/o.q 8
 
1.5%
ca 8
 
1.5%
sc/paris 7
 
1.3%
17608 5
 
0.9%
a/5 5
 
0.9%
2 5
 
0.9%
w./c 5
 
0.9%
2343 4
 
0.7%
Other values (383) 445
82.0%
2025-06-19T19:42:11.061027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 364
12.7%
1 311
10.8%
2 268
9.3%
7 207
 
7.2%
6 206
 
7.2%
0 204
 
7.1%
5 195
 
6.8%
4 188
 
6.5%
8 144
 
5.0%
9 137
 
4.8%
Other values (22) 650
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 364
12.7%
1 311
10.8%
2 268
9.3%
7 207
 
7.2%
6 206
 
7.2%
0 204
 
7.1%
5 195
 
6.8%
4 188
 
6.5%
8 144
 
5.0%
9 137
 
4.8%
Other values (22) 650
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 364
12.7%
1 311
10.8%
2 268
9.3%
7 207
 
7.2%
6 206
 
7.2%
0 204
 
7.1%
5 195
 
6.8%
4 188
 
6.5%
8 144
 
5.0%
9 137
 
4.8%
Other values (22) 650
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 364
12.7%
1 311
10.8%
2 268
9.3%
7 207
 
7.2%
6 206
 
7.2%
0 204
 
7.1%
5 195
 
6.8%
4 188
 
6.5%
8 144
 
5.0%
9 137
 
4.8%
Other values (22) 650
22.6%

Fare
Real number (ℝ)

Distinct169
Distinct (%)40.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean35.627188
Minimum0
Maximum512.3292
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-06-19T19:42:11.166582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.2292
Q17.8958
median14.4542
Q331.5
95-th percentile151.55
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.6042

Descriptive statistics

Standard deviation55.907576
Coefficient of variation (CV)1.5692391
Kurtosis17.921595
Mean35.627188
Median Absolute Deviation (MAD)6.825
Skewness3.6872133
Sum14856.538
Variance3125.6571
MonotonicityNot monotonic
2025-06-19T19:42:11.289187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.75 21
 
5.0%
26 19
 
4.5%
13 17
 
4.1%
8.05 17
 
4.1%
7.8958 11
 
2.6%
10.5 11
 
2.6%
7.775 10
 
2.4%
7.225 9
 
2.2%
7.2292 9
 
2.2%
8.6625 8
 
1.9%
Other values (159) 285
68.2%
ValueCountFrequency (%)
0 2
 
0.5%
3.1708 1
 
0.2%
6.4375 2
 
0.5%
6.4958 1
 
0.2%
6.95 1
 
0.2%
7 2
 
0.5%
7.05 2
 
0.5%
7.225 9
2.2%
7.2292 9
2.2%
7.25 5
1.2%
ValueCountFrequency (%)
512.3292 1
 
0.2%
263 2
 
0.5%
262.375 5
1.2%
247.5208 1
 
0.2%
227.525 1
 
0.2%
221.7792 3
0.7%
211.5 4
1.0%
211.3375 1
 
0.2%
164.8667 2
 
0.5%
151.55 2
 
0.5%

Cabin
Text

Missing 

Distinct76
Distinct (%)83.5%
Missing327
Missing (%)78.2%
Memory size3.4 KiB
2025-06-19T19:42:11.481257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length3
Mean length4.0769231
Min length1

Characters and Unicode

Total characters371
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)68.1%

Sample

1st rowB45
2nd rowE31
3rd rowB57 B59 B63 B66
4th rowB36
5th rowA21
ValueCountFrequency (%)
f 4
 
3.4%
b57 3
 
2.5%
b63 3
 
2.5%
b66 3
 
2.5%
b59 3
 
2.5%
b45 2
 
1.7%
c25 2
 
1.7%
c27 2
 
1.7%
c78 2
 
1.7%
c23 2
 
1.7%
Other values (80) 92
78.0%
2025-06-19T19:42:11.757657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 43
11.6%
5 34
9.2%
1 33
 
8.9%
B 32
 
8.6%
6 30
 
8.1%
3 28
 
7.5%
27
 
7.3%
2 25
 
6.7%
4 21
 
5.7%
7 15
 
4.0%
Other values (8) 83
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 371
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 43
11.6%
5 34
9.2%
1 33
 
8.9%
B 32
 
8.6%
6 30
 
8.1%
3 28
 
7.5%
27
 
7.3%
2 25
 
6.7%
4 21
 
5.7%
7 15
 
4.0%
Other values (8) 83
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 371
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 43
11.6%
5 34
9.2%
1 33
 
8.9%
B 32
 
8.6%
6 30
 
8.1%
3 28
 
7.5%
27
 
7.3%
2 25
 
6.7%
4 21
 
5.7%
7 15
 
4.0%
Other values (8) 83
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 371
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 43
11.6%
5 34
9.2%
1 33
 
8.9%
B 32
 
8.6%
6 30
 
8.1%
3 28
 
7.5%
27
 
7.3%
2 25
 
6.7%
4 21
 
5.7%
7 15
 
4.0%
Other values (8) 83
22.4%

Embarked
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
S
270 
C
102 
Q
46 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ
2nd rowS
3rd rowQ
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Length

2025-06-19T19:42:11.842855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:42:11.898517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s 270
64.6%
c 102
 
24.4%
q 46
 
11.0%

Most occurring characters

ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Interactions

2025-06-19T19:42:07.791487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:05.164436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:05.864599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.332947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.824229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:07.329198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:07.868892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:05.240837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:05.939294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.411989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.906254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:07.406313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:07.947808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:05.315036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.014975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.491763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.990644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:07.482422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:08.028207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:05.391399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.093993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.574822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:07.076350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:07.564842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:08.111519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:05.495600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.179583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.662673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:07.165581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:07.645387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:08.186833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:05.575922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.254529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:06.744267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:07.243523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:42:07.718030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-19T19:42:11.961059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeEmbarkedFareParchPassengerIdPclassSexSibSpUnnamed: 0
Age1.0000.1350.315-0.130-0.0190.3490.000-0.015-0.019
Embarked0.1351.0000.2400.1130.0600.3080.1090.1010.060
Fare0.3150.2401.0000.3780.0200.4750.1540.4410.020
Parch-0.1300.1130.3781.0000.0510.0000.2130.4120.051
PassengerId-0.0190.0600.0200.0511.0000.0540.000-0.0101.000
Pclass0.3490.3080.4750.0000.0541.0000.1060.1130.054
Sex0.0000.1090.1540.2130.0000.1061.0000.1360.000
SibSp-0.0150.1010.4410.412-0.0100.1130.1361.000-0.010
Unnamed: 0-0.0190.0600.0200.0511.0000.0540.000-0.0101.000

Missing values

2025-06-19T19:42:08.307512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-19T19:42:08.410783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-19T19:42:08.523961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
008923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
118933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
228942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
338953Wirz, Mr. Albertmale27.0003151548.6625NaNS
448963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
558973Svensson, Mr. Johan Cervinmale14.00075389.2250NaNS
668983Connolly, Miss. Katefemale30.0003309727.6292NaNQ
778992Caldwell, Mr. Albert Francismale26.01124873829.0000NaNS
889003Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.00026577.2292NaNC
999013Davies, Mr. John Samuelmale21.020A/4 4887124.1500NaNS
Unnamed: 0PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
40840813003Riordan, Miss. Johanna Hannah""femaleNaN003349157.7208NaNQ
40940913013Peacock, Miss. Treasteallfemale3.011SOTON/O.Q. 310131513.7750NaNS
41041013023Naughton, Miss. HannahfemaleNaN003652377.7500NaNQ
41141113031Minahan, Mrs. William Edward (Lillian E Thorpe)female37.0101992890.0000C78Q
41241213043Henriksson, Miss. Jenny Lovisafemale28.0003470867.7750NaNS
41341313053Spector, Mr. WoolfmaleNaN00A.5. 32368.0500NaNS
41441413061Oliva y Ocana, Dona. Ferminafemale39.000PC 17758108.9000C105C
41541513073Saether, Mr. Simon Sivertsenmale38.500SOTON/O.Q. 31012627.2500NaNS
41641613083Ware, Mr. FrederickmaleNaN003593098.0500NaNS
41741713093Peter, Master. Michael JmaleNaN11266822.3583NaNC